Dynamic memory-based communication in disaggregated datacenters

ABSTRACT

Embodiments are provided herein for dynamic memory-based communication in a disaggregated computing system. A pool of similar computing elements is configured as a large address space, the large address space segmented by an identifier. Data travel distances are optimized depending on a historical or expected use of a data object by using a grouping and amortization algorithm to relocate the data object within the pool of similar computing elements at a particular address within the large address space according to the historical or expected use.

CROSS-REFERENCE TO RELATED APPLICATIONS

This Application is related to the following eight Applications havingU.S. patent application Ser. Nos. 16/141,835, 16/141,842, 16/141,762,16/141,851, 16/141,855, 16/141,870, 16/141,874, and 16/141,878, eachfiled on even date as the present Application.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates generally to large scale distributedcomputing, and more particularly, to efficient component communicationtechniques and optimizing resource utilization in disaggregatedcomputing systems.

Description of the Related Art

A popular type of large scale computing is cloud computing, in whichresources may interact and/or be accessed via a communications system,such as a computer network. Resources may be software-renderedsimulations and/or emulations of computing devices, storage devices,applications, and/or other computer-related devices and/or services runon one or more computing devices, such as a server. For example, aplurality of servers may communicate and/or share information that mayexpand and/or contract across servers depending on an amount ofprocessing power, storage space, and/or other computing resources neededto accomplish requested tasks. The word “cloud” alludes to thecloud-shaped appearance of a diagram of interconnectivity betweencomputing devices, computer networks, and/or other computer relateddevices that interact in such an arrangement.

Cloud computing may be provided as a service over the Internet, such asin the form of “Infrastructure as a Service” (IaaS), “Platform as aService” (PaaS), and/or “Software as a Service” (SaaS). IaaS maytypically provide physical or virtual computing devices and/oraccessories on a fee-for-service basis and onto which clients/users mayload and/or install, and manage, platforms, applications, and/or data.PaaS may deliver a computing platform and solution stack as a service,such as, for example, a software development platform, applicationservices, such as team collaboration, web service integration, databaseintegration, and/or developer community facilitation. SaaS may deploysoftware licensing as an application to customers for use as a serviceon demand. SaaS software vendors may host the application on their ownclouds or download such applications from clouds to cloud clients,disabling the applications after use or after an on-demand contractexpires.

The provision of such services allows a user access to as much in theway of computing resources as the user may need without purchasingand/or maintaining the infrastructure, such as hardware and/or software,that would be required to provide the services. For example, a user mayinstead obtain access via subscription, purchase, and/or otherwisesecuring access. Thus, cloud computing may be a cost effective way todeliver information technology services. However, cloud computing mayalso be hindered by issues of resource configuration and allocationaspects, and hence, there is a fundamental need to enhance and improveupon the structures and systems supporting these services.

SUMMARY OF THE INVENTION

Various embodiments for dynamic memory-based communication in adisaggregated computing system, by a processor, are provided. In oneembodiment, a method comprises (a) configuring a pool of similarcomputing elements as a large address space, the large address spacesegmented by an identifier; and (b) optimizing data travel distancesdepending on a historical or expected use of a data object by using agrouping and amortization algorithm to relocate the data object withinthe pool of similar computing elements at a particular address withinthe large address space according to the historical or expected use.

In addition to the foregoing exemplary embodiment, various other systemand computer program product embodiments are provided and supply relatedadvantages. The foregoing Summary has been provided to introduce aselection of concepts in a simplified form that are further describedbelow in the Detailed Description. This Summary is not intended toidentify key features or essential features of the claimed subjectmatter, nor is it intended to be used as an aid in determining the scopeof the claimed subject matter. The claimed subject matter is not limitedto implementations that solve any or all disadvantages noted in thebackground.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the advantages of the invention will be readilyunderstood, a more particular description of the invention brieflydescribed above will be rendered by reference to specific embodimentsthat are illustrated in the appended drawings. Understanding that thesedrawings depict only typical embodiments of the invention and are nottherefore to be considered to be limiting of its scope, the inventionwill be described and explained with additional specificity and detailthrough the use of the accompanying drawings, in which:

FIG. 1 is a block diagram illustrating a hardware structure of acomputing system, according to aspects of the present disclosure;

FIG. 2 is a block diagram illustrating an exemplary cloud computingenvironment, according to aspects of the present disclosure;

FIG. 3 is a block diagram illustrating abstraction model layers,according to aspects of the present disclosure;

FIG. 4 is a block diagram illustrating a hardware structure of adisaggregated computing environment, according to aspects of the presentdisclosure;

FIG. 5 is an additional block diagram illustrating a hardware structureof a disaggregated computing environment, according to aspects of thepresent disclosure;

FIGS. 6A-6B are block diagrams illustrating a traditional network andfabric communication architecture, according to aspects of the presentdisclosure;

FIGS. 7A-7E are block diagrams illustrating component level buildingblocks for constructing a dynamically-wired communication architecture,according to aspects of the present disclosure;

FIGS. 8A-8F are block diagrams illustrating various processor pool tomemory pool communication techniques within the dynamically-wiredarchitecture, according to aspects of the present disclosure;

FIG. 9 is a block diagram illustrating an exemplary component groupingscheme for partitioning a communication pattern, according to aspects ofthe present disclosure;

FIG. 10 is a flowchart diagram illustrating a method of a system processassociated with a known communication pattern, according to aspects ofthe present disclosure;

FIG. 11 is a flowchart diagram illustrating a method of a system processassociated with an unknown communication pattern, according to aspectsof the present disclosure; and

FIG. 12 is a flowchart diagram illustrating a method for efficientcomponent communication and resource utilization in a disaggregatedcomputing system, according to aspects of the present disclosure.

DETAILED DESCRIPTION OF THE DRAWINGS

In any computing environment, and particularly in large-scaledistributed models, retrieving data from memory or storage to beprocessed and then returned for storage is the most fundamental functionof the computing system. The manner in which mechanisms are implementedto perform this task, however, vary widely from one another asprocessing elements are inherently different in their optimizationaspects, construction, technology and packaging when compared with thatof storage or memory elements and devices. When considering this task,location and latency are key, as the closer the processing elements tothe data objects they need to process (and the more efficiently theprocessing elements are able to access this data), the more optimizedthe system will be. Optimally, it would be advantageous to avoid copyingdata over a fabric or network, be that a local area network (LAN) or asymmetric multiprocessor (SMP) type of links between multiple computingelements. Rather, the closest positioning of the processing elements tomemory is to create a physical point-to-point, direct link between thatmemory storing the data and the processing elements. Such connectionsexist as hardwired (fixed) attachment, for example, between a processorchip to the nearby memory devices (e.g., dual in-line memory modules(DIMMs)) where the processor's memory controller directly drives andmanages the memory devices.

In a distributed or parallel processing architecture, however, thisformer model is not feasible. This is because, for any computation,scientific engineering for high-performance computing (HPC) or businesstransaction, and/or performance of analytic and cognitive insights,different relationships are created between processing elements andmemory elements which are required in the course of the computation.These relationships between multiple different processing and memoryelements are inherently more than a fixed hardwired connection can allowaccess to, which is known as a fundamental problem of the memory wall.To wit, the more processing elements (e.g., processor cores) which arecreated and need to process data, the more connections to such data isneeded, as this data is typically spread over many different memorydevices. However the fixed and anything-to-anything connections used inpast architectures, cannot scale economically and functionally.

Consider partitioning data into data objects, such that the need toaccess the information represented by an object while accessing someother data objects, by a common processing element, changes over time.Here, a data object is a collection of information that is required fora computation. This information is viewed as an object because the groupof data structures it is representing is being used to compute resultsby performing computations that may need only the group of datastructures being represented, or to be computed properly it may requireother such data object groups which were devised to partition the dataof the input problem among a large number of memory devices in aparticular system (or datacenter). The concurrent computation performedon data objects which are crafted in the latter manner (data stored onmultiple devices) usually involve a “network” that transfers data oroutput from different places in the system, as needed for thecomputation. Since typically processing elements (e.g., processordevices) have directly attached (in a fixed way) memory devices, therebecomes a challenge to partition the overall data the concurrentcomputations may need to access (and/or change by storing intermediateresults) into groups of objects in a manner such that differentprocessing elements (or mixes of different processing elements) haveimmediate access to in the course of computation.

Accordingly, modern computing architectures do not meet the needs oflarge scale big data computing requirements, as will be furtherdiscussed below. Thus, to create a completely new paradigm for thecomputation and data distribution/storage issues as aforementioned, aradically new architecture is needed that changes how computation isperformed, how the data needed for these computations are accessed fromvarious memory devices, and ultimately how systems and datacenters areorganized. Hence, the present disclosure outlines many different andnovel techniques for data communication within large scale datacentersand so-called “disaggregated” systems (discussed following). Thesetechniques may use any type of “memory” device, including bothintermediate storage (e.g., DIMMs) and devices which may be defined as“storage” at current (e.g., disk and tape drives, flash storage, etc.).Further, these techniques may be applied to any type of processingelement device, including accelerators (graphical processing units(GPUs), field-programmable gate arrays (FPGAs), etc.) or regularprocessors (e.g., central processing units (CPUs)), which may be of anyinstruction set architecture (ISA) and perform transformations orprocessing on the information stored by the memory devices. Theaforementioned techniques and mechanisms disclosed herein may begeneralized into the following categories:

(a) Generic types of memory controllers and computing devices: Thecommunication and rewiring functionality disclosed herein allows thecreation of a new system from a different type of architecture at thecomponent level. As will be described, a computing element may implementdirect “general purpose links”, that connect its package to the rest ofthe disaggregated computing system. That is, instead of having a fixedpartition of connecting bandwidth to memory, SMP, input/output (I/O) andnetworking communications, as it is performed at present, these novelgeneral purpose links can be used for any type of protocol hardwareswitched on their physical layer. The use of new technologies such asSilicon Photonics to achieve high bandwidth densities and connectivityallow signals to travel at distances from resources within the samedrawer to the furthest resources by traveling across racks, and thewhole datacenter if needed.

Further, the use of all optical cross connect switches can direct thesegeneral purpose links to the right resources, including other processingelements, storage, or memory as needed. The “building block” components,as will be further described, may comprise processing elements (e.g.,CPUs, GPUs, FGPAs, etc.), memory elements (each having differingproperties, e.g., flash, 3D non-volatile memory, and dynamic randomaccess memories (DRAMs)), and packet switch types (each supportingvarious protocols such as Ethernet, Infiniband, memory load/storetransactions, etc.) and others. Another type of switch comprising acircuit switch may be used to connect these building blocks in atransparent and agnostic way. That is, various combinations of theseelements may be connected dynamically through one or more of the generalpurpose links, regardless of a specific hardware protocol, softwareprotocol, or connection configuration parameter set used to transferdata therebetween. In short, the links created between these elements(which may be dynamically changed and switched to various other elementson-demand) comprise a dynamic wiring that enables point-to-point, speedof light data transfer with no buffering or routing through a typicalSMP bus.

(b) Dynamic memory based communication: The disclosed mechanisms includea platform to allow optimization of data distances depending on the dataobject's past use or expected use as it relates to memory, Phase ChangeMemory, Flash and other storage/main memory types of devices. Theprocessing elements access stored data as a large address space that issegmented with an identifier. That is, the processing elements do notexplicitly request for the data (e.g., files, objects, etc.), but ratherprovide an address. The data is then rearranged within the memory to beas physically close or far to the processing elements as its use casemerits. While traditional object storage allows a memory-likeorganization of storage data without the need for a file system and theassociated overhead thereof, the techniques used in a disaggregatedsystem perform this functionality more efficiently without the drawbacksof current architectures. Moreover, grouping and amortization throughthe memory may be used as the main connection to the processingelements, increasing efficiency even further. To wit, the disclosedfunctionality establishes dynamic memory based connections to enablegeneric communication between processing elements as needed todynamically increase the system's utilization by alleviating thecontention of the traditional network architecture. The disclosedgrouping and amortization algorithms of connections between resourcepools may provide additional benefit between the memory elements as themain connection to processing elements (having previously establishedconnections to the memory elements) may further be used for secondaryconnections. In this way, memory elements for performing thecommunication may be allocated based on a distance of the particularmemory assigned to processing elements which will compute the underlyingdata stored therein to minimize rewiring overhead and maximize generallink usage.

(c) Grouping of communications: The discussed techniques use grouping ofdifferent, unrelated computing and/or memory type of devices. Sharedlinks may then be used to form a connection by any one of the devices inone group to communicate with any one of the other devices in the othergroup(s). This grouping will increase the link utilization in caseswhere the communication needed is using a small fraction of theestablished link bandwidth between the two (or more) groups. Moreover,the associated relaying of communications may be performed over furtherdistances than group to group at increased efficiency, as thecommunication will still be faster than using a network/switch based andprotocol stack in software.

(d) Utilization of everything: The disclosed functionality providesmechanisms to utilize every resource comprised within the datacenter.While efficiently yet fully utilizing processing and memory typeresources is important, the techniques herein focus especially onconnectivity bandwidth links that connect between component types tocompose disaggregated systems. Since it is extremely important toutilize links associated with any given component wisely so as tomaximize the component's output, when a link is established, it islikewise imperative to utilize its link bandwidth to the fullestpossible. Hence, disclosed are techniques to aggregate use from multipleresources of the same type to amortize the setup of these connectionsbetween resource pools. Thus for certain type of connections, such asin-memory communication (versus using read/write links to/from memory asif it was local to a computing device), the dynamically createdconnections may be shared between links. Although these links may beshared, they are shared securely through encryption of a common memorylocation with same pairs of encryption keys (for a same service levelagreement (SLA)/user). To wit, multiple links may be shared (for examplebetween components, SLAs, and/or users) yet the data within a given linkis secured by using the common memory location which is encrypted. Inthis way, users/tenants having a particular SLA, for example, may accessthe link securely just as if it were a dedicated link through use of aparticular set of same encryption keys associated and known to theuser/tenant or SLA.

(e) In-line accelerators versus block accelerators: The disclosedmechanisms further describe how computing elements may be used asaccelerators in two ways. The first way is by connecting a firstprocessing element with another processing element. This may beachieved, for example, through a coherent SMP type of link, in whichcase, the accelerator shares the main computing element visibility tomemory and is to perform efficient acceleration measured by theutilization of the accelerator and the bandwidth of the link connectingit to the other computing element. The second way is for an acceleratorto have an independent local memory, copy chunks of information quicklyform memory pools at the direction of the main computing element, andthen signal and copy back the information to the memory when acomputation is finished (as typical GPUs perform currently, forexample). In this latter case, the connection is generally formed to agroup of accelerators that will share the pool connections to do suchdata copies form memory pools, and then perform computations on theinformation from local memory subsequent to the copy. It should benoted, however, that, in performing this way, connections needed areused by multiple accelerators and the communication is relayed throughmemory pools where the computing elements have been connectedpreviously.

Disaggregated System Description

The techniques of this disclosure preferably are implemented within thecontext of a “disaggregated” computing system wherein a “disaggregatedserver”—sometimes referred to herein as a “server entity”—is dynamicallyconstructed/composed or constitutes server resources selected from (orassigned from) shared server resource pools, namely, one or more of: aprocessor or CPU pool, a memory pool, an accelerator pool (e.g., a GPUaccelerator, a network accelerator, etc.), and a storage pool. As thenomenclature suggests, a “compute” pool typically constitutes physicalprocessors (such as CPUs), a “memory” pool typically constitutesphysical memory devices (such as DIMMs), etc. A given shared poolpreferably includes just the particular resource types, but a particularresource pool may be composed of one or more resource sub-types. Thenotion of a “pool” is not intended to be limiting, as the commonresources may be collected, aggregated or otherwise combined in anysuitable manner. Further, a “pool” may be a dedicated set of resourcesthat have the common type or sub-type, or some ad hoc collection of suchresources. Preferably, a particular server entity comprises serverresources from one or more of the server resource pools.

Disaggregated computing systems provide flexibility and elasticity inconstructing bare-metal computing systems for use in the cloud, toprovide on-demand flexibility to cloud users, or “tenants”. Asmentioned, a disaggregated computing system is referred to as a systemwith large pools of physical hardware resources, such as CPUs,accelerators, memory devices, and storage devices, whose connectivitywith each other individual hardware resource can be dynamically switchedwithout shutting down any hardware nor running applications. Individualhardware resources from these pools can be selected to assemble computersystems on-demand. Thus, a bare-metal computer system with a flexiblecapacity of individual computing resources may be assembled in adisaggregated system, such that workloads are computed based on hardwareresource configurations that are most suitable for the respectiveworkload. In one embodiment, for example, a system may be constructedwith an extremely high capability of memory size but with a moremoderate capacity of CPU and other resources, for a memory-intensiveworkload. This functionality is enabled by the use of point-to-pointcircuit wire level switching. In other words, components, on abare-metal wire level (e.g., using optical memory architecturefunctionality), are connected in mere milliseconds to assemble a givensystem or allocate/de-allocate individual components of the givensystem. All disaggregated system proposals currently known in the artare copy-based systems in which a process state is copied over a memoryfabric to local memory at the computation hardware because of thelatency in connecting directly to the pool over the memory fabric orover a symmetric multiprocessing (SMP) system. This means that switchingbetween computing components cannot happen in milliseconds as can thepoint-to-point circuit wire level switching mechanisms used in thepresent invention thereby enabling a continuous, instantaneous executionof processes even while the computing components are switched.

One advantageous component of disaggregated computing systems is theopportunity to perform computation between various hardware resources inways previously unattainable. For example, in most pre-configuredcomputing systems, pre-fetching techniques and data locality help tokeep cache hit rates high, enabling ultra-fast performance for the enduser. However, if the processor spends a vast amount of time finding theneeded data in the cache, it may be under-utilizing bandwidth to mainmemory. Since the disaggregated architecture permits additionalprocessing components to be connected to various memory pool modules, amethod to better utilize this bandwidth to memory modules is proposed byefficiently connecting to the memory modules from other processingcomponents (during times of low usage) to perform analytic functionswhich may lead to valuable insights about the data, or its processing.Such memory access will not pass through the usual SMP fabric connectingprocessors, and hence does not disturb inter-processor communication andcoherency when really needed, increasing efficiency further.

In various embodiments, instead of the memory access passing through theSMP fabric connecting resources, this process may be facilitated by theuse of optical links. For example, in some embodiments, each of theprocessors and memory elements (and/or other components of thedisaggregated system) share a number of optical external links. Theseexternal links are made for optimizing a point-to-point connectionwithin the optical-switching fabric at very high bandwidth. Thisoptimization may be in the physical implementation used, or in theprotocol chosen to facilitate such high bandwidth, and preferably it hasthe ability to support memory switching within one physical link ormultiple physical links to look like one high bandwidth physical linkmade of a few physical links. Because these external links typically arecircuit-switched via at least one optical switch that will not be awareof the data or content thereof, these should use a very lightweightcommunication protocol.

The physical properties of these external links may require the use ofmultiple optical wavelengths in a WDM (wavelength division multiplexer),which are all coupled into one fiber or one external link, but areseparable at both ends. The mirror-based micro electro mechanical system“MEMS” optical circuit switch “OCS” will deflect in the optics domain,the light beams within these external links, regardless of their numberof wavelength, protocol, and signaling speed. Preferably, and in theembodiment depicted, these external links are common to all memoryblades and processor blades.

In one architecture, at least one optical circuit switch is sharedbetween the optical external links. Also, several independent circuitsmay be established between the processors and the memory blades sharingthe optical circuit switch. These external links are made for optimizinga point-to-point connection at very high bandwidth. This optimizationmay be in the physical implementation used in the protocol chosen tofacilitate such high bandwidth and has the ability to supportaggregation of multiple streams within one physical link or multiplephysical links to look like one high bandwidth physical link made of afew physical links. Because these external links are circuit switchedvia an all optical switch that will not be aware of the protocol, dataor content thereof, a very light weight communication protocol is used.Furthermore, the physical properties of these external links may requirethe use of multiple optical wavelengths in a WDM (wavelength divisionmultiplexer), which are all coupled into one fiber or one external link,but are separable at both ends. The mirror-based micro electromechanical system “MEMS” optical circuit switch “OCS” will deflect, inthe optics domain, the light beams within these external linksregardless of their number of wavelength, protocol, and signaling speed.These external links are common to all processors, blades, memory, andindependent circuits, such that any memory blade/processor blade maypass information on one or all of these external links, either directlyor by passing through the interconnected processor blades. In oneexemplary embodiment, circuit-switching switches are used. Circuitswitching switches do not need to switch frequently, and thus may bemuch simpler to build, and can use different technologies (e.g., alloptical, MEMS mirror based) to dynamically connect between the circuits,memory, and processor blades.

These types of external links and the dynamic switching enable very highthroughput (e.g., high bandwidth) connectivity that dynamically changesas needed. As multi-core processing chips require very high bandwidthnetworks to interconnect the multi-core processing chips to other suchphysical processing nodes or memory subsystem, the exemplaryoptically-connected memory architecture plays a vital role in providinga solution that is functionally enabled by the memory switchingoperations.

In another example, and in the context of the present invention, thearchitecture of disaggregated computing systems may be leveraged todynamically construct a server entity of various physical resourcesaccording to the physical locality of the data and the underlyingresources needed to complete workloads utilizing this data. Considerthat typical resource allocation mechanisms would attempt, for a singlecomputer system, to allocate resources that are physically close to oneanother to reduce system latency. However, depending on a workload'sdata access patterns executed by this computer system, these allocations(even as they may be physically close together) may have little or noeffect on performance and could lead to fragmented and non-optimalresults for the larger disaggregated framework (as the actual localityof the underlying data may be different than the locality of theresources performing the workload). Accordingly, considered is amechanism for continual resource allocation optimization which leveragesobserved system behavior (e.g., data access patterns) and the unique,resource allocation capabilities of the disaggregated system todynamically re-align processing resources to data in a way not possiblein traditional systems. This re-alignment of system resources will serveto strike a better balance between the overall disaggregated frameworkutilization and the performance of each dynamic hardware system.

It should be noted that the instant disclosure, for brevity, mayfrequent the language of “resources”, “components”, and/or “elements”.In an actual implementation of the present invention, the resources,components, or elements termed herein may be comprised of CPUs (orportions of CPUs such as individual processor cores), GPUs, memory,storage devices, network devices, accelerator devices, etc. which are,again, generally pooled together in a shared resource pool fashion.Indeed, any hardware and/or software resources as commonly known in theart are to be construed interchangeably with “resources”, “components”,“elements”, and/or “resource types” as described herein, as onepracticing the art would appreciate.

Typically, the shared resource pools are available within the physicalconfines of a particular datacenter, although this likewise is not alimitation. Thus, the shared resource pools themselves may be sharedacross physical datacenters. Further, a particular server entity is notrequired to be composed of resources from each of the server pools.

By way of background, but not by way of limitation, the followingdescribes a representative computer environment in which the techniquesof this disclosure (described below) may be practiced.

Turning now to FIG. 1, exemplary architecture 10 of a general computingenvironment in which the disaggregated compute system of this disclosuremay be implemented and/or comprised of is depicted. The computer system10 (which may also be referred to as “cloud computing node” 10) includesCPU 12, which is connected to communication port 18 and memory device16. The communication port 18 is in communication with a communicationnetwork 20. The communication network 20 and storage network may beconfigured to be in communication with computer systems (hosts) 24 and22 and storage systems, which may include storage devices 14. Thestorage systems may include hard disk drive (HDD) devices, solid-statedevices (SSD) etc., which may be configured in a redundant array ofindependent disks (RAID). The operations as described below may beexecuted on storage device(s) 14, located in system 10 or elsewhere andmay have multiple memory devices 16 working independently and/or inconjunction with other CPU devices 12. Memory device 16 may include suchmemory as electrically erasable programmable read only memory (EEPROM)or a host of related devices. Memory device 16 and storage devices 14are connected to CPU 12 via a signal-bearing medium. In addition, CPU 12is connected through communication port 18 to a communication network20, having an attached plurality of additional computer systems 24 and22. In addition, memory device 16 and the CPU 12 may be embedded andincluded in each component of the computing system 10. Each storagesystem may also include separate and/or distinct memory devices 16 andCPU 12 that work in conjunction or as a separate memory device 16 and/orCPU 12.

It is further understood in advance that although this disclosureincludes a detailed description on cloud computing, following, thatimplementation of the teachings recited herein are not limited to acloud computing environment. Rather, embodiments of the presentinvention are capable of being implemented in conjunction with any othertype of computing environment now known or later developed.

As previously eluded to, cloud computing is a model of service deliveryfor enabling convenient, on-demand network access to a shared pool ofconfigurable computing resources (e.g. networks, network bandwidth,servers, processing, memory, storage, applications, virtual machines,and services) that can be rapidly provisioned and released with minimalmanagement effort or interaction with a provider of the service. Thiscloud model may include at least five characteristics, at least threeservice models, and at least four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes and storage systems (e.g. storagesubsystem 20).

Referring now to FIG. 2, illustrative cloud computing environment 52 isdepicted. As shown, cloud computing environment 52 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 52 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 52 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 52 (FIG. 3) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 80 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 81; RISC(Reduced Instruction Set Computer) architecture based servers 82;servers 83; blade servers 84; storage devices 85; and networks andnetworking components 86. In some embodiments, software componentsinclude network application server software 87 and database software 88.

Virtualization layer 90 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers91; virtual storage 92; virtual networks 93, including virtual privatenetworks; virtual applications and operating systems 94; and virtualclients 95.

In one example, management layer 100 may provide the functions describedbelow. Resource provisioning 101 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 102provides cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 103 provides access to the cloud computing environment forconsumers and system administrators. Service level management 104provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 105 provides pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 110 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 111; software development and lifecycle management 112;virtual classroom education delivery 113; data analytics processing 114;transaction processing 115; and, in the context of the illustratedembodiments of the present invention, various resource monitoring andcommunication functionality 116. One of ordinary skill in the art willappreciate that the resource monitoring and communication functionality116 may also work in conjunction with other portions of the variousabstractions layers, such as those in hardware and software 80,virtualization 90, management 100, and other workloads 110 (such as dataanalytics processing 114, for example) to accomplish the variouspurposes of the illustrated embodiments of the present invention.

Turning now to FIG. 4, a block diagram of a disaggregated computingarchitecture is illustrated, of which is included in the cloud computingenvironment 52. Within cloud computing environment 52 is thedisaggregated computing system comprising physical hardware resources200. Physical hardware resources 200 may comprise of classifications ofthe hardware resources such as a storage device pool 202, a GPU devicepool 204, a CPU device pool 206, a memory device pool 208, and a networkdevice pool 210. The physical hardware resources 200 are incommunication with a management module 250. Management module 250 maycomprise of such components as an individual resource provisioningcomponent 252 and a resource monitor 254, each described herein.Further, the management module 250 is in communication with acommunications orchestration component 260, which may include componentssuch as a communication manager 262, a communication monitor 264, arewiring manager 266, and a network grouping component 268. It should benoted that the management module 250 and the communicationsorchestration component 260 (and associated components therein,respectively) may form one management entity or may comprise separateand distinct entities. In either scenario, the aforementioned componentand modules associated with the management module 250 and thecommunications orchestration component 260 may work in concert toperform various aspects of the present invention which will be describedhereinbelow.

In communication with the cloud computing environment 52, the managementmodule 250, and the physical hardware resources 200, are tenants 212A,212B, and 212 n. Tenants 212A, 212B, and 212 n may communicate with thecloud computing environment 52 by way of the management module 250, andthus the physical resources 200 provided by any signal-bearing medium.

It should be noted that the elements illustrated in FIG. 4 provide onlyan example of related components that may be included in thedisaggregated computing architecture. For example, management module 250may include other components than individual resource provisioningcomponent 252 and resource monitor 254, and physical hardware resources200 may include other component classifications than storage device pool202, GPU device pool 204, CPU device pool 206, and memory device pool208, and network pool 210, while staying in spirit and scope of thepresent invention. Additionally, the duties of the management module250, and thus the components therein, may be performed and comprised ofphysical components, computer code, or a combination of such.

In one embodiment, the management module 250 interacts with individualtenants 212A-n to receive workload requests and locate the best suitablehardware resources for the given workload through use of the individualresource provisioning component 252. Individual hardware resources ofthe physical hardware resources 200 may be tracked by the resourcemonitor 254 and a mapping be maintained between each respective tenant212A-n and each respectively assigned hardware resource. Each hardwareresource is identified using a unique identifier. This identifier may bea physical identifier (e.g., barcode) and/or a virtual identifier (e.g.,code based). The management module 250, or any other suitable modules ormeans known in the art may be used to accomplish these mechanisms.

In some embodiments, as previously discussed, the communicationsorchestration component 260 may form one entity with the managementmodule 250 and/or work in concert as a distinct entity to performvarious aspects denoted herein. The communication monitor 264, forexample, monitors and detects traffic patterns of data transactedbetween a set of grouped servers and sends information related to thesetraffic patterns to the communication manager 262. The communicationmanager 262 may then determine whether to form a more effective networkby dynamically reconnecting (rewiring) processors and memories throughvarious links based on the detected communication pattern, as will befurther described. When it is determined that a new communicationnetwork should be dynamically created, the communication manager 262 maysend requests to the network grouping component 268 which determines themost effective processor grouping scheme that may be used to form adynamic network. Subsequent to the determined processors beingpartitioned into groups by the network grouping component 268, therewiring manager 266 may be assigned the task of allocating a sharedmemory, and processor and memory links which need to be establishedthrough the individual resource provisioning component 252 to establishthe connection. The rewiring manager 266 may then instruct themanagement component 250 to execute the rewiring scheme. Finally, thenewly established network can be used for the targeted communication toimprove the communication efficiency.

FIG. 5 is a block diagram illustrating the physical hardware resources200 portion of FIG. 4. Included in the storage device pool 202 arestorage devices 202A, 202B, and 202 n. The GPU device pool 204 includesGPU devices 204A, 204B, and 204 n. The CPU device pool 206 includes CPUdevices 206A, 206B, and 206 n. The memory device pool 208 includesmemory devices 208A, 208B, and 208 n. Finally, the network device pool210 includes network devices 210A, 210B, and 210 n. Each aforementionedhardware resource may be in communication with an additional one or moreaforementioned hardware resources via a signal-bearing medium.

Within physical hardware resources 200, each hardware resource appearingin solid line (i.e., storage device 202A, GPU device 204A, CPU device206A, memory device 208A, and network device 210A) are assigned hardwareresources to one or more tenants (i.e., tenants 212A, 212B, 212 n).Hardware resources appearing in dashed line (i.e., storage devices 202B,202 n, GPU devices 204B, 204 n, CPU devices 206B, 206 n, memory devices208B, 208 n, and network devices 210B, 210 n) are unassigned hardwareresources which are available on-demand for a respective tenant 212A-nworkload.

Each respective tenant 212A-n may be assigned individual respectivehardware resources 200 in arbitrary quantities. In one embodiment, eachrespective tenant 212A-n may be assigned an arbitrary quantity of anindividual respective hardware resource 200 within a limit of totalsystem capacity and/or an available quantity of the respective hardwareresources 200. For example, a memory device 208A-n allocated from thememory pool to a respective tenant 212A-n may be provided in a minimalunit of allocation (e.g., a byte or word) up to a limit of total systemcapacity and/or an available quantity of the memory devices 208A-n.

In another embodiment, each respective tenant 212A-n may be assignedindividual respective hardware resources 200 within a quantum stepsizing restriction. For example, memory devices 208A-n may need to beallocated on quantum sizes of full or half of memory DIMM units, toassure full bandwidth from the respective memory device 208A-n to theprocessor when reading/writing data. This is especially true in adisaggregated system since the memory device 208A-n is directlyconnected via fiber/optical switch to the processor memory unit (forread/write memory transactions) as if it was locally connected to theprocessor chip, but rather may be a small distance (e.g., 1 meter) awayin location. In another example, because the disaggregated system is notbased on virtual components but rather physical components (i.e., actualchips than cores or VMs), the quantum sizing restriction may requirethat a minimum of one CPU device 206A-n be assigned to a tenant 212A-n,with additional CPU devices 206A-n being provisioned to the tenant212A-n in two, four, etc. quantities.

Dynamic Component Communication in Disaggregated Datacenters

Having described one or more architectures in which the functionality ofthe present invention may employ and as previously discussed, in variousembodiments, the functionality of the present invention leverages thepoint-to-point circuit wire level switching and hardware poolingattributes of disaggregated systems to provide an efficient mechanismand architecture for resource communication. As aforementioned, in thistype of disaggregated system, like resources are organized into poolswhich can be directly connected to resources of another pool. Aneffectively provisioned “system” will be composed of a set of computingresources connected in a point-to-point fashion to memory and storageresources, etc.

By way of background, the paradigm of shared memory or symmetricmultiprocessing has created an architecture whereby different computingelements can access data from different memory devices where the dataresides, even if the memory elements are not directly connected to theprocessing elements that need to process the data. This is, aspreviously described, generally performed through a network or a fabricwhich is to be directly implemented mostly by hardware circuits orfirmware/microcode engines, which facilitate the illusion that allprocessing elements can share one another's local memory content ifneeded. Of course, latency to access the data through such a networkbecomes very high, and as with the usual single computation element,caches are used to bring data closer to the processing elementsperforming the computation as needed. However, if this data is beingused/accessed and changed by more than one processing element, the localcache copies within the processing element need be invalidated suchthat, when needed, a fresh copy of the data that has changed is readagain from the specific memory device on which it is stored.

Given that the fabric is to allow any-to-any connections, trafficbetween pairs of memory devices and processing devices can be very high,being limited the through scaling of such an architecture. Hence ifprocessing elements were to only access data that is not directlyconnected to their processor chips locally, the bandwidth requirementswill scale to levels that cannot be economically and practicallyimplemented. For example, in the IBM® Power8 system, the highest-endmodel comprises 8 memory banks, each capable to handle 200 Gigabits/secread bandwidth and about 100 Gigabits/sec of write bandwidth. As shownin diagram 600 of FIG. 6A, in a 16-socket Power8 SMP (labeled withprocessors 602A-n), the total memory bandwidth of the connectedprocessors 602A-n would amount to only a fraction of the sum of thebandwidth which all memory banks of these 32 sockets can use. Astechnology allows for denser chipsets where more processing elements arecreated within chips, and with higher density packaging used (such as 3Dintegration, silicon substrates, etc.), a processor socket is deprivedmore and more to scale processing power. That is, as technologyimproves, the connections between the processing elements to otherdevices (to access data to be computed) do not support the bandwidth sumof all circuit cores and accelerators realized on their own siliconchips.

The use of a scale out architecture, illustrated as diagram 650 of FIG.6B, is even worse than the aforementioned SMP model or scale up cases.In a scale out architecture, multiple servers (referenced as servers652A-n) are typically connected to a network in a fixed and complex waythat may include a software stack used to move data from one server“box” to another. Again, these connections are again fixed so thattypically a server is connected to a top of rack (ToR) switch, usingEthernet or Infiniband protocols. A number of ToR switches may furtherbe connected in switching hierarchies (e.g., using a folded Clostopology or equivalent) in a lead/spine type of connection. Therefore,referring to diagram 650, it is inherent that memory holding states(e.g., application states) in one server 652A-n may take a lengthyamount of time to access from computing elements in another server652A-n, even if both servers 652A-n are connected to the samefirst-switching ToR hierarchy.

In recent years, a new paradigm has been pushed by various proposals inthe industry, some of which are called disaggregated systems. However,in many so-called disaggregated systems, the problem of componentcommunication remains unsolved. While in the ultimate case vision,disaggregated systems comprise resource pools which provide a physicalseparation between processing devices (which can be any deviceperforming a computation, including accelerators such as GPUs, FPGAs,and specialty accelerators performing artificial intelligence (AI) anddeep learning tasks) and memory devices (where “memory” may be differenttechnologies including traditional main memory types to various storagetechnologies), the problem described above is again unsolved. This isbecause even in some disaggregated architectures, to access data fromany memory device place efficiently by any processing device types thedata still passes through a fabric or a network that has the samedrawbacks as the aforementioned case of the SMP fabric for scale uparchitectures. Typically these systems are using fixed local attachedmemory to processing elements, and the fabric is used to copy chunks ofdata from memory pools to local fixed attached memory devices which areplaced at the processing element's sockets. Hence these architecture donot fully “disaggregate” memory from processing elements and still relyon copying data back and forth. The reason this is problematic has to dowith the ability to quickly move resources (e.g., processing elements)from one workload/SLA, for example, to another SLA/user without havingto copy back the local memory content thereby freezing the state of theSLA for future use (nor the need to copy a previously suspended state ofan SLA from a memory pool to reactivate the state if it is neededquickly). Hence, the agility and elasticity corresponding with real-timeevents cannot be matched, and the utilization of these resources willnot be as high as was the case with previous generations of datacentersand servers.

Generic Types of Memory Controllers and Computing Devices

The disaggregated approach of the present disclosure differs from thosearchitectures previously described by truly “disaggregating” theprocessing and memory elements by way of facilitating genericcommunication between all components. That is, the disclosed techniquessolve previous deficiencies by uncoupling the need for data to be copiedback and forth to processing elements and memory devices, and ratherimplement a novel communication system which does not need to transferdata information through the traditional SMP fabric. It should be notedthat the functionality of the present disclosure can be applied to anytype of “memory” device and any type of “computing element”, be itdigital or analog in nature of the stored data therein or thecomputation carried out by the computing elements.

First defined are a set of generic types of processing and data storecomponents with various types of links, which will be further described.FIGS. 7A-7E depict what may be described as “building blocks” formingthe fundamental base of this communication architecture. Now referringto FIG. 7A, illustrated is a processor building block 700 which containsgroups of processors (e.g., selected from the CPU device pool 206 and/oras a “drawer” of processors within a rack) which are connected to oneanother with SMP links. Each processing or processing element (e.g.,depicted processor device 206A) is additionally equipped with a set (ofa certain number, depending on the device or element) of “generalpurpose links” which can be dynamically configured to connect to varioustypes of devices, components, or elements on-demand. These generalpurpose links may also be referred to as “stem cell links” because oftheir inherent ability to connect and differentiate between multipletypes of links between multiple types of devices (i.e., a generalpurpose link or “stem cell” link is not fixed in its use case, protocol,or connectivity to a certain type of device, element, or component). Forexample, the general purpose links can be configured as a graphicalprocessing link of a graphical processing fabric (e.g., an NVLink™)connecting the processing elements to GPU devices. The general purposelinks may further comprise memory load store links connecting theprocessing elements to memory DIMMs, I/O links connecting the processingelements to storage devices, or SMP links connecting the processingelements to other processors. These general purpose links are connectedto an optical switch 702 in order to communicate with other components(e.g., the memory, storage, accelerator devices, etc.) outside of theCPU device pool 206 drawer; and processors transferring information withone another within the same pool or drawer communicate through theprocessor backplane (not depicted). It should be again noted that thesegeneral purpose links can be dynamically configured (in substantiallyreal-time) based on the needs of the devices involved and the datacommunicated, thus the system becomes more flexible and the linkutilization can be significantly improved.

Again, when referring to these component building blocks in a typicalfixed system (a non-disaggregated system), they comprise modules orchips within a socket that are connected to each other in a fixed way.For example, a CPU will have certain dedicated connections to memorymodules, certain dedicated connections to other CPUs, and certaindedicated connections for external devices known as I/O such as PCIExpress. The design and purpose of these connections is not changeablytypically to best adapt to the changing needs of the workloads runningon such components. While there are limited reuse of externalconnections (for example, using the PCI Express physical pins out of aprocessor socket to double as an NVLink™ special protocol, although theconnections are very different in nature) such are fixed once a systemis built into a server box. With the advancement of fabricationtechnology (which creates smaller and smaller transistors, and in-socketpackaging technologies), connecting together multiple chip dies on asubstrate or on multiple stacks of chips on top of one another, a systemin a socket (versus in a chip) becomes very complex and data hungry. Thetrend of bottleneck of connectivity between system in a socket and othersuch devices, will only become more severe even with advancements ofphysical connection technology such as the use of optical modules withinthe socket.

Thus, the present invention uses these building blocks to provide ageneric and common carrier connection that can physically be assignedany specific protocol through either software and/or dedicated hardwarecircuits on the dies connected within the socket. The aim is to changethe personality and use of all connections in a dynamic fashion toutilize as much as possible the total of bandwidth coming from/to asocket as possible. Hence, for example, memory intensive computationsmay provision multiple general purpose connections (links) to connect tomemory types of resources. Intensive computations may require multipleof these general purpose connections to act as processor-to-processorconnections, which therefore connecting to other compute resources. Ineach case, a different protocol and different parameters of suchconnections will be implemented with special circuits, and may beswitched dynamically as the personality of the general purposeconnections change with time during the execution of different workloadsand different workload types. The timing and configuration of thisdynamic rewiring may be determined by multiple means, such asidentifying a specific or general data traffic pattern learned over ahistorical period used by specific workloads. In other examples, therewiring may be triggered by a user's directive, observed pressure overa threshold of communication (e.g., for bandwidth, latency, performance,and throughput), a need to access a maximum number of data sets fromdifferent stored locations in memory after which a cache state is formedfor a phase of computation, and more.

Advancing, FIG. 7B demonstrates the connections of a memory buildingblock 720. Inherently, within the disaggregated architecture there aremultiple memory cards within a pool (e.g., memory devices 208A-ndepicted in memory pool 208) or drawer. A memory card or device isgenerally composed of a memory controller and multiple memory DIMMs. Thememory DIMMs within the same memory cards communicate through the memorycontroller 704 controlling the connected cards or devices thereto. DIMMslocated on different memory cards communicate through the memorycontrollers each of the memory DIMMs are attached to. Controllers ondifferent memory cards within the same pool or drawers communicatethrough the memory backplane 706; and controllers on different memorypools or drawers communicate through the optical switch 702.Accordingly, the links of memory controllers can be dynamically rewiredto connect to different processor drawers or different memory drawersbased on the communication needs of the allocated applications.

FIG. 7C illustrates a switch building block 740. Unlike the traditionaldata centers where the switches are statically configured and linked asspine-leaf network architecture (as shown in FIG. 6B), the packageswitches are instead organized into package switch pool drawers 742.Within each package switch pool drawer 742, each switch has a number oflinks (general purpose or stem cell links) which can be dynamicallyreconfigured to connect to different devices, components, or elements(within the same pool/drawer or other pools/drawers) as needed.

Continuing, storage building blocks have a similar architecture as thememory building blocks 720 illustrated in FIG. 7B. FIG. 7D depicts astorage building block 760 where, similar to the memory controller 704,the storage controller(s) 708 are responsible for managing storagedevices (e.g., storage devices 202A-n depicted in storage pool 202)within the storage pool drawer, such as disk drives, NV flash, etc. Thestorage controller 708 communicates through the storage backplane 710 ifthe storage devices therein are transacting data within the same drawer.Otherwise, storage controller 708 may communicate through optical switch702 to other storage controllers (in other pools or drawers), the memorypool 208 drawers, or CPU pool 206 of processor drawers, etc. Again, thecommunication through the links of the storage controller 708 may bedynamically reconfigured to communicate data to other devices,components, or elements (within the same pool/drawer or otherpools/drawers) as needed within the disaggregated system.

Finally, FIG. 7E illustrates an accelerator building block 780, whichcontains one or more accelerator controllers 714 and accelerators (e.g.,GPU devices 204A-n depicted in GPU pool 204) within the accelerator pooldrawer. The accelerators may include local memory 712 attached to speedup the processing efficiency of the respective accelerators within thedrawer. Like the previous examples, the accelerator controller 714 maycommunicate and transact data to other controllers within the same poolor drawer through an accelerator backplane (not depicted), or to otheraccelerator controllers in different (separate) pools or drawers throughthe optical switch 702. The general purpose links used by theaccelerator controller 714 may be dynamically configured as a memorylink, graphical processing fabric link (e.g., an NVLink™), an I/O link,etc. dependent upon which devices the accelerator controller 714connects to. Similar to those aforementioned other building blockexamples, these links may be dynamically reconfigured (“rewired”) at anytime based on the needs of the communication and data required thereof.

Dynamic Memory-Based Communication

As mentioned, unlike traditional data centers where networks are wiredstatically, disaggregated systems support dynamically changed (switched)connections between processors and memory. FIG. 8A illustrates howcommunications between components/elements can benefit from the dynamicrewiring capability of the disaggregated system, which inherentlyseparates the communication traffic from the regular network. Thisenables processors to read data through different memory elementswithout requiring that data to pass through the traditional SMP network,and thereby, in a sense, bringing differing processors which arephysically further apart from one another closer to each other. In thedata communication architecture 800 of FIG. 8A within the disaggregatedsystem, consider that one or more of the processors n 206 and one ormore of the processors n′ 206′ need to communicate and exchange data fora certain period (or are expecting to exchange certain data for acertain period). Consider also that one of or more of the processors n206 is currently connected to another memory pool drawer (memory pool208′). In this scenario, the system rewires the two (or more) processorsto communicate through memory m within the (same) memory pool 208. Thismay be performed by way of forming a connection of the general purposelink of the one or more processors n 206 through the optical switch 702to the one or more processors n′ 206′ through memory m. Thus, the datacommunication neither passes through multiple hops nor the regularnetwork, and creates a highly efficient and low latency connection whencompared to existing solutions.

In some cases, the rewiring capability may be used to move processing ormemory elements closer to a workload associated with a particular SLA.That is, data objects may be monitored according to their current orpast use (or a defined future use), and the disclosed rewiringmechanisms may take this information to make certain decisions aboutwhere processing and memory elements which execute this workload shouldbe physically located. Ideally, it is advantageous to have processingelements as close as possible to the underlying data objects in whichthey are performing computations on. Thus, the mechanisms of the presentdisclosure may be used to leverage the rewiring and communicationtechniques disclosed herein to optimally “place” the underlying dataassociated with a particular workload, SLA, or tenant/user closer to theprocessing element which will compute such. Likewise, the disclosedcommunication techniques may be similarly used to advantageouslydetermine and reconfigure those memory or storage elements/devices whichthe data may be distributed thereon.

As utilizing this dynamic rewiring capability can potentially increasethe communication efficiency by dynamically creating processorcommunication groups through shared memories, it is not clear how todesign such a generic communication framework leveraging the dynamicrewiring capability using current state of the art approaches. Thus, themechanisms of the present invention generate an entirely newarchitecture by first establishing communication between two processinggroups followed by partitioning and grouping the communications groups.FIGS. 8B-8F depict different communication establishing schemes givendifferent combinations of locations between the processor group and thememory group.

One exemplary communication architecture may comprise a situation wheretwo separate processor pools having one allocated memory poolcommunicate through the memory backplane 706. In one embodiment, asdepicted in architecture 815 of FIG. 8B, when two processor groups arelocated on separate processor pools (processors n 206 and processors n′206′) while the allocated memory elements thereto (shown as the link insolid line) these two processor groups are within the same memory pooldrawer (e.g., memory pool 208), the memory-based connection isestablished by allocating a memory element m (which is referenced as thememory element m in the memory pool 208 encompassed in vertical dashedlines) with the same memory pool 208 drawer of the two processor groups.In this way, the two processor groups can then communicate throughmemory element m by simply dynamically configuring the two links betweenthe memory controllers 704 and the memory backplane 706, therebyenabling the two memory controllers to communicate. The linksrepresented in horizontal dashed line and solid line between theprocessor groups and the memory pool 208 drawer may then be reused forother purposes.

Another exemplary scenario may comprise a situation where one processorpool having two separate memory pools allocated thereto communicatesthrough an SMP link. Accordingly, in another embodiment, as depicted inarchitecture 830 in FIG. 8C, when the two processor groups are locatedwithin the same processor pool (e.g., within the processors n 206pool/drawer) while the allocated memory elements thereto (shown as thelink in solid line) are located on separate memory pool drawers (e.g.,memory pool 208 and memory pool 208′), the memory-based connection isestablished by allocating a memory element m (which is referenced as thememory element m in the memory pool 208 encompassed in vertical dashedlines) in one of the corresponding memory pool drawers (e.g., memorypool 208). The data communication is therefore established from theprocessors n 206 through the optical switch 702 to the memory element mwithin the memory pool 208 drawer. In this way, the two processor groupsmay then communicate through this memory element m by simply dynamicallyconfiguring the links between the processors n 206 (shown as the “links”between the processors n 206 in horizontal dashed line and theprocessors n 206 in the solid line) and the link traveling through theoptical switch 702 to the memory element m of the memory pool 208drawer. This enables the processors n 206 to form SMP links to oneanother and to the memory element m (again, depicted in the verticaldashed lines within memory pool 208). The remaining links shown in solidline to the memory pool 208′ may then be reused in this case to accessthe memory element m encompassed in the vertical dashed line.

Yet another exemplary scenario may comprise a situation where oneprocessor pool having one memory pool allocated thereto communicatesthrough SMP links or the memory backplane 706. Therefore, in anotherembodiment, as depicted in architecture 845 in FIG. 8D, when twoprocessor groups are located on the same processor pool (e.g.,processors 206) while the allocated memory thereto (shown as the link insolid line) is within the same memory pool drawer (e.g., memory pool208), the memory-based communication is established by allocating amemory element m (which is referenced as the memory element m in thememory pool 208 encompassed in vertical dashed lines) within the samememory pool drawer (e.g., memory pool 208) of the two processor groups.The processors n 206 encompassed within the solid line may then accessthe memory encompassed in the horizontal dashed line either through thelinks configured at the memory backplane 706 or through the load-storelinks connecting the two processor groups (shown as “links” between theprocessor groups within processors n 206). This additional flexibilityincreases the link utilization and lowers any associated costs byconcurrently utilizing all link bandwidth from the two processing groupsto their respective memory elements.

Still another exemplary scenario may comprise a situation where twoseparate processor pools having memory elements within two separatememory pools communicate through shared memory optical links. Hence, inanother embodiment, as depicted in architecture 860 in FIG. 8E, when twoprocessor groups (one on each pool of processors n 206 and processors n′206′) and the allocated memory (including memory elements within memorypool 208 and memory elements within memory pool 208′) thereto arelocated on the separate pools, the memory-based connected is establishedby a memory element m (which is referenced as the memory element m inthe memory pool 208 encompassed in vertical dashed lines) to eithermemory pool drawer (i.e., memory pool 208 or memory pool 208′). Adynamic point-to-point connection may then be configured to enable thememory controller 704 of the memory element m within the memory pool 208(encompassed in horizontal dashed line) and the additional memorycontroller (not shown) to additional memory element within memory pool208′ (encompassed in vertical dashed lines) to communicate. In this waythe processor group within the processors n′ 206′ (shown in horizontaldashed line) may access the additional memory element within the memorypool 208′ through the link formed between the memory controller 704 andthe optical switch 702, and the optical switch 702 and the memory pool208′.

FIG. 8F illustrates yet an additional option for a scenario where twoseparate processor pools having memory elements within two separatememory pools communicate through processor-shared optical links. In thiscase, depicted in architecture 875 of FIG. 8F and similar to thearchitecture 860 of FIG. 8E, when both processor groups and theallocated memory thereto are located on the separate pools, theprocessor group within processors n′ 206 (shown in horizontal dashedline) now accesses the additional memory element within the memory pool208′ (depicted in vertical dashed lines) through the link establishedbetween the processors n 206 and the processors n′ 206 through theoptical switch 702. These shared links through the optical switch 702then allow either processing group of either processors n 206 (shown insolid line) or processors n′ 206′ (shown in horizontal dashed lines) toaccess the memory elements of either memory pool 208 (shown inhorizontal dashed lines) or the memory elements within memory pool 208′(shown in vertical dashed lines). That is, the link formed at theoptical switch 702 allows the system to choose and reroute traffic usingthe most efficient path. The system chooses one of the link optionsbased on resource availability, the rewiring cost (i.e., whether thecost of the utilization of resources used to perform the reconfigurationoutweighs the anticipated gain in performance) and the bandwidthutilization of the links (e.g., if one link has high bandwidthutilization, it may be advantageous to perform the reconfigurationthrough the alternate link).

Grouping of Communications

As mentioned in the system architecture of FIG. 4, the communicationmonitor 264, may monitor and detect traffic patterns of data transactedbetween a set of grouped servers and sends information related to thesetraffic patterns to the communication manager 262. The communicationmanager 262 may then determine whether to form a more effective networkby dynamically reconnecting (rewiring) processors and memories throughvarious links based on the detected communication pattern, as will befurther described. When it is determined that a new communicationnetwork should be dynamically created, the communication manager 262 maysend requests to the network grouping component 268 which determines themost effective processor grouping scheme that may be used to form adynamic network. Subsequent to the determined processors beingpartitioned into groups by the network grouping component 268, therewiring manager 266 may be assigned the task of allocating a sharedmemory, and processor and memory links which need to be establishedthrough the individual resource provisioning component 252 to establishthe connection. The rewiring manager 266 may then instruct themanagement component 250 to execute the rewiring scheme. Finally, thenewly established network can be used for the targeted communication toimprove the communication efficiency.

The reasoning for partitioning the processors into groups are twofold.Firstly, the number of links required to be reconfigured (rewired) toestablish the network may be reduced, as the processors within a givengroup may share the link. In this way, both the resource consumption andthe time required to perform the reconfiguration may be reduced.Secondly, if the processors within each group are located within thesame processor pool, these processors can communicate much moreefficiently using the inter-processor links in the backplane of theprocessor pool. As the disaggregated system owns the ability to exchangeprocessors between processor pools by rewiring the connections thereofwithout copying any data, the capability to bring grouped processorsinto the same processor pool can thereby be leveraged. Thus, the networkgrouping component 268 not only partitions processors into certainprocessor groups, but also exchanges processors from other groups toform the certain processor groups if necessary.

In some embodiments, the network grouping component 268 groupsprocessors based on data traffic patterns and the location of theinvolved processors. In particular, the communication patternsconsidered include, however are not limited to, binary tree, shuffle,broadcast, and scatter and gather patterns. FIG. 9 depicts a groupingpattern 900, illustrating an example of how a binary tree communicationpattern is partitioned into five groups. The grouping pattern ofrespective processor devices 206A-n referenced as “A” shows which of therespective processor devices 206A-n are grouped into the fiveestablished groups (circled) by the network grouping component 268. Thegrouping pattern referenced as “B” shows the links and the communicationscheme formed between the processing groups (depicted with largerdirectional arrows). Subsequent to the processor devices 206A-n beinggrouped, the groups may then be connected using a shared memory, andtherefore only one link within a processor pool needs to bereconfigured. Thus, the complexity of the reconfiguration is O(n), inwhich n is the number of groups.

When the processors within a group are not in the same processor pool,the rewiring manager 266 finds a destination processor pool which holdsthe maximum number of processors within the group, and creates exchangerequests for the processor requests that are not in the same pool. Foreach of the exchange requests, the rewiring manager 266 identifies aprocessor in the targeted pool and exchanges the links between theseprocessors and associated memory devices.

After processors are grouped as a graph, for each inter-group link, therewiring manager 266 then proceeds to retrieve allocated memory elementsfrom the management component 250, chooses the earlier availableprocessor link from each processor group and reconfigures the processorto the allocated memory. Ideally, the two processor links should bereconfigured to the same memory controller if a sufficient number oflinks is available at the memory controller in which the allocatedmemory resides. Otherwise, one or both links may be rewired to the samememory drawer. If no connection is available, the system waits for tmilliseconds, and otherwise fails the request.

System Application Programming Interface (API) and Process

In some embodiments, the system API may comprise the following commands:handle comm=register_communication(type, list src[ ], list dest[ ], datasize) register the communication: type:broadcast, gather, shuffle,binary tree

Void deregister_communication(handle comm): de-register thecommunication when it is finished

Void useNetwork(comm):the following communication go through theconnection of comm.

Void unuseNetwork( ) the communication go through the defaultconnection.

According to the system API and the aforementioned system architecture,FIG. 10 is a flowchart diagram illustrating a method 1000 of a systemprocess associated with a known communication pattern, as previouslydiscussed. The method 1000 (and all subsequent methods disclosed herein)may be performed in accordance with the present invention in any of theenvironments depicted in FIGS. 1-9, among others, in variousembodiments. Of course, more or fewer operations than those specificallydescribed in FIG. 10 may be included in the methods, as would beunderstood by one of skill in the art upon reading the presentdescriptions.

Each of the steps of the method 1000 (and all subsequent methodsdisclosed herein) may be performed by any suitable component of theoperating environment. For example, in various embodiments, the method1000 may be partially or entirely performed by a processor, or someother device having one or more processors therein. The processor, e.g.,processing circuit(s), chip(s), and/or module(s) implemented in hardwareand/or software, and preferably having at least one hardware componentmay be utilized in any device to perform one or more steps of the method1000. Illustrative processors include, but are not limited to, a CPU, anApplication Specific Integrated Circuit (ASIC), a Field ProgrammableGate Array (FPGA), etc., combinations thereof, or any other suitablecomputing device known in the art.

The method 1000 starts (step 1002) by submitting a communication requestthrough the system API (e.g., a shuffle pattern) (step 1004). Thecommunications manager 262 accepts the communication requests andretrieves the physical location of all involved processors (step 1006).The network grouping component 268 is then instructed to calculate agrouping scheme (step 1008). The communications request is then sent tothe rewiring manager 266 which dynamically reconfigures the linksbetween the processors and memory elements, establishes the new linkconnection, and returns a success receipt via the communications request(step 1010). The method 1000 ends (step 1012).

FIG. 11 is a flowchart diagram illustrating a method 1100 of a systemprocess associated with an unknown communication pattern, of which isdetermined upon monitoring. The method 1100 starts (step 1102) withmonitoring, by the communications monitor 264, the traffic patternbetween various processing and memory elements (step 1104). Thecommunication manager 262 then retrieves the monitored traffic patterninformation from the communications monitor 264 (step 1106). Adetermination is made as to whether the duration of the traffic patternexceeds a certain threshold (step 1108). This threshold may beassociated with link utilization, bandwidth, time, or a certain trafficpattern in its entirety (e.g., shuffle, etc.). If, at step 1108, theduration does not exceed the threshold, the method 1100 returns to step1104 where the traffic pattern is monitored by the communicationsmonitor 264. If, however, at step 1108, the duration exceeds the certainthreshold, a reconfiguration (rewiring) request is triggered, and theentirety of the method 1000 is performed (step 1110). That is, thecommunication manager 262 accepts the triggered communication requestand retrieves the physical locations of all involved processors. Thenetwork grouping component 268 is then used to calculate a groupingscheme and forward the communication request to rewiring manager 266 todynamically reconnect the links between the respective processors andmemories. Subsequent to the connection being established, thecommunication manager 262 returns success. The data communicationthereafter is transparently switched to use the newly establishednetwork. The method 1100 ends (step 1112).

Whole-System Utilization

As mentioned, the disclosed functionality provides mechanisms to utilizeevery resource comprised within the datacenter. Since it is extremelyimportant to utilize links associated with any given component wisely soas to maximize the component's output, when a link is established, it islikewise imperative to utilize its link bandwidth to the fullestpossible. Hence, the described links may be aggregated for use frommultiple resources of the same type to amortize the setup of theseconnections between resource pools. Thus for certain type ofconnections, such as in-memory communication (versus using read/writelinks to/from memory as if it was local to a computing device), thedynamically created connections may be shared between links. This linksharing applies to elements of respective resource pools (e.g.,connections currently established between memory elements in memorydevice pool 208 and processing elements within CPU device pool 206),such that the already-established links/connections may be used tofacilitate data transmission of workloads from multiple uniquelycomposed disaggregated systems belonging to respective users or tenants(e.g., tenants 212A-n) in lieu of establishing independent links foreach disaggregated system. In any case, the sharing of these connectionsand links may be further facilitated by the use of an amortizationalgorithm which prioritizes the efficiency of sharing the links to thecost of rewiring these links to establish new connections, as discussedpreviously. To wit, the amortization algorithm may resist performing anyrewiring of any links or connections unless it can be identified thatthe cost of the performance of the rewiring and setup of a newconnection would outweigh the cost of merely sharing the existingconnection of the computing elements between multiple users/tenants.

Although these links may be shared, they are shared securely throughencryption of a common memory location with same pairs of encryptionkeys (for a same SLA)/user). To wit, multiple links may be shared (forexample between components, SLAs, and/or users) yet the data within agiven link is secured by using the common memory location which isencrypted. In this way, users/tenants having a particular SLA, forexample, may access the link securely just as if it were a dedicatedlink through use of a particular set of same encryption keys associatedand known to the user/tenant or SLA.

In-Line Accelerators Vs. Block Accelerators

As further mentioned, computing elements which comprise accelerators maybe used in two different ways. The first way is by connecting a firstprocessing element with another processing element. This may beachieved, for example, through a coherent SMP type of link, in whichcase, the accelerator shares the main computing element visibility tomemory and is to perform efficient acceleration measured by theutilization of the accelerator and the bandwidth of the link connectingit to the other computing element.

The second way is for an accelerator to have an independent localmemory, copy chunks of information quickly from memory pools at thedirection of the main computing element, and then signal and copy backthe information to the memory when a computation is finished (as typicalGPUs perform currently, for example). In this latter case, theconnection is generally formed to a group of accelerators that willshare the pool connections to do such data copies form memory pools, andthen perform computations on the information from local memorysubsequent to the copy. It should be noted, however, that, in performingthis way, connections needed are used by multiple accelerators and thecommunication is relayed through memory pools where the computingelements have been connected previously. Thus, the group of acceleratorsare resources that do not need to maintain their connection to memorypools for much time, other than to retrieve the data needed for aparticular workload, place it into a, local directly attached memory ofthe accelerator, and stream back the resulting output of thecomputation. Because of this situation, connections between memoryelements and the multiple accelerators in the pool may be shared tomaximize their utilization and allow streaming back of output to thememory. Further, the connections may be shared to allow the loading ofnew data to process from different accelerators that may serve differentusers (tenants), yet share the connections with proper security (e.g.encrypted data with different keys).

Generalizing the concepts outlined previously, FIG. 12 is a flowchartdiagram illustrating a method 1200 for efficient component communicationand resource utilization in the disaggregated computing system. Themethod 1200 starts (step 1202) by configuring a pool of similarcomputing elements as a large address space, the large address spacesegmented by an identifier (step 1204). Data travel distances areoptimized depending on a historical or expected use of a data object byusing a grouping and amortization algorithm to relocate the data objectwithin the pool of similar computing elements at a particular addresswithin the large address space according to the historical or expecteduse (step 1206). The method 1200 ends (step 1208).

Commensurate with the optimization of data travel distances, asdiscussed in FIGS. 8A-8F, the aim is to store data objects as close tothe responsible underlying processing components (which will executethese data objects) as possible. That is, depending on a learned historyof the usage of this data and a context of the workloads which the datais constituent thereto, the general purpose links may be dynamicallyrewired (in real time) to facilitate the establishment of a mosteffective path to/from the processing components to the memory elementsstoring the data objects. In this way, latency is mitigated because theprocessing components have a direct point-to-point connection to thememory elements to retrieve and execute the data objects stored therein.The system may implement various algorithms and analyses (e.g., machinelearning, correlation algorithms, etc.) to monitor workloads and theirobject data to determine which portions of the data objects a respectiveworkload is expected to retrieve/execute and at what point in time, anddynamically rewire the processing components executing this data to thememory elements storing the data at a particular point in time so as tomore efficiently retrieve the workload (i.e., without necessitating thedata travel through multiple component/hops). Further, as mentioned,processing groups may be established and the large address space ofmemory may be implemented to shift the data within the large addressspace to a location closer to one of the processing components requiringthe data to execute its respective workload.

The present invention may be an apparatus, a system, a method, and/or acomputer program product. The computer program product may include acomputer readable storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outaspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

What is claimed is:
 1. A method for dynamic memory-based communicationin a disaggregated computing system, by a processor, comprising:configuring a pool of similar computing elements aggregately as a singlelarge address space, the single large address space segmented only by anidentifier, wherein the pool of similar computing elements comprises afirst memory pool composed exclusively of a plurality of memory devicesand remotely located to the processor; and optimizing data traveldistances depending on a historical or expected use of a data object byusing a grouping and amortization algorithm to relocate the data objectwithin the pool of similar computing elements at a particular addresswithin the single large address space according to the historical orexpected use.
 2. The method of claim 1, wherein the relocation isperformed upon the processor accessing the data object at the particularaddress.
 3. The method of claim 2, wherein the processor is a firstprocessor; and further including connecting the first processor and asecond processor to at least a first memory element within the firstmemory pool through an optical switching device.
 4. The method of claim3, wherein the first processor is within a first processor pool and thesecond processor is within a second processor pool; and wherein thefirst and second processors communicate with one another through a firstmemory block in the first memory element.
 5. The method of claim 3,wherein the first and second processors are each within a firstprocessor pool; and wherein the first and second processors communicatewith one another through a first memory block in the first memoryelement using a symmetric multiprocessing (SMP) link.
 6. The method ofclaim 4, wherein the first and second processors communicate with oneanother through the first memory block selectively using a symmetricmultiprocessing (SMP) link or a memory backplane.
 7. The method of claim3, wherein the first processor is within a first processor pool and thesecond processor is within a second processor pool; and furtherincluding providing a second memory element within a second memory pool,wherein the first and second processors communicate through the secondmemory element using a shared memory optical link or a shared opticalswitch link.
 8. A system for dynamic memory-based communication in adisaggregated computing system, comprising: a pool of similar computingelements; and a processor; wherein the processor: configures the pool ofsimilar computing elements aggregately as a single large address space,the single large address space segmented only by an identifier, whereinthe pool of similar computing elements comprises a first memory poolcomposed exclusively of a plurality of memory devices and remotelylocated to the processor; and optimizes data travel distances dependingon a historical or expected use of a data object by using a grouping andamortization algorithm to relocate the data object within the pool ofsimilar computing elements at a particular address within the singlelarge address space according to the historical or expected use.
 9. Thesystem of claim 8, wherein the relocation is performed upon theprocessor accessing the data object at the particular address.
 10. Thesystem of claim 9, wherein the processor is a first processor; andfurther including connecting the first processor and a second processorto at least a first memory element within the first memory pool throughan optical switching device.
 11. The system of claim 10, wherein thefirst processor is within a first processor pool and the secondprocessor is within a second processor pool; and wherein the first andsecond processors communicate with one another through a first memoryblock in the first memory element.
 12. The system of claim 10, whereinthe first and second processors are each within a first processor pool;and wherein the first and second processors communicate with one anotherthrough a first memory block in the first memory element using asymmetric multiprocessing (SMP) link.
 13. The system of claim 11,wherein the first and second processors communicate with one anotherthrough the first memory block selectively using a symmetricmultiprocessing (SMP) link or a memory backplane.
 14. The system ofclaim 10, wherein the first processor is within a first processor pooland the second processor is within a second processor pool; and furtherincluding providing a second memory element within a second memory pool,wherein the first and second processors communicate through the secondmemory element using a shared memory optical link or a shared opticalswitch link.
 15. A computer program product for dynamic memory-basedcommunication in a disaggregated computing system, by a processor, thecomputer program product embodied on a non-transitory computer-readablestorage medium having computer-readable program code portions storedtherein, the computer-readable program code portions comprising: anexecutable portion that configures a pool of similar computing elementsaggregately as a single large address space, the single large addressspace segmented only by an identifier, wherein the pool of similarcomputing elements comprises a first memory pool composed exclusively ofa plurality of memory devices and remotely located to the processor; andan executable portion that optimizes data travel distances depending ona historical or expected use of a data object by using a grouping andamortization algorithm to relocate the data object within the pool ofsimilar computing elements at a particular address within the singlelarge address space according to the historical or expected use.
 16. Thecomputer program product of claim 15, wherein the relocation isperformed upon the processor accessing the data object at the particularaddress.
 17. The computer program product of claim 16, wherein theprocessor is a first processor; and further including connecting thefirst processor and a second processor to at least a first memoryelement within the first memory pool through an optical switchingdevice.
 18. The computer program product of claim 17, wherein the firstprocessor is within a first processor pool and the second processor iswithin a second processor pool; and wherein the first and secondprocessors communicate with one another through a first memory block inthe first memory element.
 19. The computer program product of claim 17,wherein the first and second processors are each within a firstprocessor pool; and wherein the first and second processors communicatewith one another through a first memory block in the first memoryelement using a symmetric multiprocessing (SMP) link.
 20. The computerprogram product of claim 18, wherein the first and second processorscommunicate with one another through the first memory block selectivelyusing a symmetric multiprocessing (SMP) link or a memory backplane. 21.The computer program product of claim 17, wherein the first processor iswithin a first processor pool and the second processor is within asecond processor pool; and further including providing a second memoryelement within a second memory pool, wherein the first and secondprocessors communicate through the second memory element using a sharedmemory optical link or a shared optical switch link.